From 52dae372abe0bbc5e85a95034af89e1a80581652 Mon Sep 17 00:00:00 2001 From: Haojun Liao Date: Wed, 5 Feb 2025 16:06:24 +0800 Subject: [PATCH 1/5] Update index.md --- docs/zh/06-advanced/06-TDgpt/04-forecast/index.md | 15 +++++++++++++++ 1 file changed, 15 insertions(+) diff --git a/docs/zh/06-advanced/06-TDgpt/04-forecast/index.md b/docs/zh/06-advanced/06-TDgpt/04-forecast/index.md index 71b97aa996..087d6540e6 100644 --- a/docs/zh/06-advanced/06-TDgpt/04-forecast/index.md +++ b/docs/zh/06-advanced/06-TDgpt/04-forecast/index.md @@ -91,3 +91,18 @@ taos> select _flow, _fhigh, _frowts, forecast(i32) from foo; ## 内置预测算法 - [arima](./02-arima.md) - [holtwinters](./03-holtwinters.md) +- CES (Complex Exponential Smoothing) +- Theta +- Prophet +- XGBoost +- LightGBM +- Multiple Seasonal-Trend decomposition using LOESS (MSTL) +- ETS (Error, Trend, Seasonal) +- Long Short-Term Memory (LSTM) +- Multilayer Perceptron (MLP) +- DeepAR +- N-BEATS +- N-HiTS +- PatchTST (Patch Time Series Transformer) +- Temporal Fusion Transformer +- TimesNet From 0415f1b8ef938c1676974d678ff768e0c548f8ce Mon Sep 17 00:00:00 2001 From: Haojun Liao Date: Wed, 5 Feb 2025 16:09:38 +0800 Subject: [PATCH 2/5] Update 04-machine-learning.md --- .../06-TDgpt/05-anomaly-detection/04-machine-learning.md | 5 +++++ 1 file changed, 5 insertions(+) diff --git a/docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/04-machine-learning.md b/docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/04-machine-learning.md index ec76d6a0a3..b752d446eb 100644 --- a/docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/04-machine-learning.md +++ b/docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/04-machine-learning.md @@ -12,6 +12,11 @@ FROM foo ANOMALY_WINDOW(col1, 'algo=encoder, model=ad_autoencoder_foo'); ``` +后续添加机器（深度）学习异常检测算法 +- Isolation Forest +- One-Class Support Vector Machines (SVM) +- Prophet + ### 参考文献 1. https://en.wikipedia.org/wiki/Autoencoder From 80628c40c5ffe4d6a4fae457f73efb46e8f1b5b3 Mon Sep 17 00:00:00 2001 From: Haojun Liao Date: Wed, 5 Feb 2025 16:11:34 +0800 Subject: [PATCH 3/5] Update 03-data-density.md --- .../06-TDgpt/05-anomaly-detection/03-data-density.md | 6 +++++- 1 file changed, 5 insertions(+), 1 deletion(-) diff --git a/docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/03-data-density.md b/docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/03-data-density.md index 7c0998c917..63cbaf27ee 100644 --- a/docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/03-data-density.md +++ b/docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/03-data-density.md @@ -3,7 +3,7 @@ title: "数据密度算法" sidebar_label: "数据密度算法" --- -### 基于数据密度的检测方法 +### 基于数据密度/数据挖掘的检测算法 LOF[1]: Local Outlier Factor(LOF)，局部离群因子/局部异常因子， 是 Breunig 在 2000 年提出的一种基于密度的局部离群点检测算法，该方法适用于不同类簇密度分散情况迥异的数据。根据数据点周围的数据密集情况，首先计算每个数据点的一个局部可达密度，然后通过局部可达密度进一步计算得到每个数据点的一个离群因子， 该离群因子即标识了一个数据点的离群程度，因子值越大，表示离群程度越高，因子值越小，表示离群程度越低。最后，输出离群程度最大的 $topK$ 个点。 @@ -15,6 +15,10 @@ FROM foo ANOMALY_WINDOW(foo.i32, "algo=lof") ``` +后续待添加基于数据挖掘检测算法 +- DBSCAN (Density-Based Spatial Clustering of Applications with Noise) +- K-Nearest Neighbors (KNN) + ### 参考文献 1. Breunig, M. M.; Kriegel, H.-P.; Ng, R. T.; Sander, J. (2000). LOF: Identifying Density-based Local Outliers (PDF). Proceedings of the 2000 ACM SIGMOD International Conference on Management of Data. SIGMOD. pp. 93–104. doi:10.1145/335191.335388. ISBN 1-58113-217-4. From dae497c42909a80bf338e92d242ac887ebaa8acc Mon Sep 17 00:00:00 2001 From: Haojun Liao Date: Wed, 5 Feb 2025 16:13:51 +0800 Subject: [PATCH 4/5] Update 02-statistics-approach.md --- .../05-anomaly-detection/02-statistics-approach.md | 7 +++++++ 1 file changed, 7 insertions(+) diff --git a/docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/02-statistics-approach.md b/docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/02-statistics-approach.md index d0d6815c25..4055d42572 100644 --- a/docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/02-statistics-approach.md +++ b/docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/02-statistics-approach.md @@ -50,6 +50,13 @@ FROM foo ANOMALY_WINDOW(foo.i32, "algo=shesd,direction=both,anoms=0.05") ``` +后续待添加异常检测算法 +- Gaussian Process Regression + +基于变点检测的异常检测算法 +- CUSUM (Cumulative Sum Control Chart) +- PELT (Pruned Exact Linear Time) + ### 参考文献 1. [https://en.wikipedia.org/wiki/68–95–99.7 rule](https://en.wikipedia.org/wiki/68%E2%80%9395%E2%80%9399.7_rule) 2. https://en.wikipedia.org/wiki/Interquartile_range From 55fdbd02d937a0f779782a50fd59255ceb57b129 Mon Sep 17 00:00:00 2001 From: Haojun Liao Date: Wed, 5 Feb 2025 16:15:28 +0800 Subject: [PATCH 5/5] Update 03-data-density.md --- .../06-TDgpt/05-anomaly-detection/03-data-density.md | 4 ++++ 1 file changed, 4 insertions(+) diff --git a/docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/03-data-density.md b/docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/03-data-density.md index 63cbaf27ee..00cd7aaa61 100644 --- a/docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/03-data-density.md +++ b/docs/zh/06-advanced/06-TDgpt/05-anomaly-detection/03-data-density.md @@ -18,6 +18,10 @@ ANOMALY_WINDOW(foo.i32, "algo=lof") 后续待添加基于数据挖掘检测算法 - DBSCAN (Density-Based Spatial Clustering of Applications with Noise) - K-Nearest Neighbors (KNN) +- Principal Component Analysis (PCA) + +第三方异常检测算法库 +- PyOD ### 参考文献